import 

import torch 
from fastai.vision.all import *
import cv2

data 

path=untar_data(URLs.PETS)/'images'

path

Path('/home/khy/.fastai/data/oxford-iiit-pet/images')
files=get_image_files(path)

def label_func(f):
    if f[0].isupper():
        return 'cat' 
    else: 
        return 'dog' 

dls=ImageDataLoaders.from_name_func(path,files,label_func,item_tfms=Resize(512)) 

Could not do one pass in your dataloader, there is something wrong in it

learn 

lrnr=cnn_learner(dls,resnet34,metrics=error_rate)
lrnr.fine_tune(1)
epoch train_loss valid_loss error_rate time
0 0.178037 0.019520 0.009472 00:34
epoch train_loss valid_loss error_rate time
0 0.039352 0.005454 0.000677 00:41

sample

1st CNN and CAM 

get_image_files(path)[0]

Path('/home/khy/.fastai/data/oxford-iiit-pet/images/boxer_128.jpg')
img = PILImage.create(get_image_files(path)[0])
img

x, = first(dls.test_dl([img]))  #이미지 텐서화

x.shape

torch.Size([1, 3, 512, 512])
net1=lrnr.model[0]
net2=lrnr.model[1] 

net2 = torch.nn.Sequential(
    torch.nn.AdaptiveAvgPool2d(output_size=1), 
    torch.nn.Flatten(),
    torch.nn.Linear(512,out_features=2,bias=False))

net=torch.nn.Sequential(net1,net2)

lrnr2=Learner(dls,net,metrics=accuracy) 

lrnr2.fine_tune(15)
epoch train_loss valid_loss accuracy time
0 0.259194 5.446237 0.415426 00:41
epoch train_loss valid_loss accuracy time
0 0.115573 0.098918 0.966847 00:41
1 0.084512 0.192782 0.924899 00:41
2 0.108377 0.263869 0.929635 00:41
3 0.108298 0.147747 0.946549 00:41
4 0.092053 0.091769 0.966170 00:41
5 0.071956 0.128430 0.957375 00:41
6 0.085712 0.074232 0.974966 00:41
7 0.055636 0.113164 0.956698 00:41
8 0.049553 0.095807 0.968200 00:41
9 0.032400 0.098665 0.966170 00:41
10 0.021480 0.058845 0.978349 00:41
11 0.014291 0.047291 0.983085 00:41
12 0.009199 0.043928 0.982409 00:41
13 0.008911 0.048288 0.982409 00:41
14 0.006678 0.049579 0.982409 00:41 
dls.vocab

['cat', 'dog']
  • (고양이,강아지)라고 생각한 확률 
a=net(x).tolist()[0][0]
b=net(x).tolist()[0][1]
np.exp(a)/(np.exp(a)+np.exp(b)), np.exp(b)/(np.exp(a)+np.exp(b))

(1.752036905842922e-05, 0.9999824796309416)
  • CAM
camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

2nd CNN and CAM

  • MODE 1 만들기

    • 가중치 재설정
test=camimg[1]-torch.min(camimg[1])

A1=torch.exp(-0.015*test)  

A2=1-A1

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A1.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A2.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

A1.data.to('cpu').shape

torch.Size([16, 16])
X1=np.array(A1.to("cpu").detach(),dtype=np.float32)

Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))

x1=x.squeeze().to('cpu')*Y1   #MODE1을 x1으로 저장

fig, (ax1,ax2) = plt.subplots(1,2) 
#
x1.squeeze().show(ax=ax1)  #MODE1
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

x1=x1.reshape(1,3,512,512)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)
camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())

camimg1.shape

torch.Size([2, 16, 16])

  • CAM

    • mode1에 CAM 결과 올리기
fig, (ax1,ax2) = plt.subplots(1,2) 
# 
x1.squeeze().show(ax=ax1)
ax1.imshow(camimg1[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
x1.squeeze().show(ax=ax2)
ax2.imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

- 첫번째 CAM결과와 비교
fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

a1=net(x1).tolist()[0][0]
b1=net(x1).tolist()[0][1]
np.exp(a1)/(np.exp(a1)+np.exp(b1)), np.exp(b1)/(np.exp(a1)+np.exp(b1))

(0.004487248125871372, 0.9955127518741286)

3rd CNN

  • MODE 2 만들기
test1=camimg1[1]-torch.min(camimg1[1])

A3=torch.exp(-0.03*test1)  

A4=1-A3

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A3.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A4.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

A3.data.to('cpu').shape

torch.Size([16, 16])
X2=np.array(A3.to("cpu").detach(),dtype=np.float32)

Y2=torch.Tensor(cv2.resize(X2,(512,512),interpolation=cv2.INTER_LINEAR))

x2=x1.squeeze().to('cpu')*Y2   #MODE2을 x2로 저장

fig, (ax1,ax2,ax3) = plt.subplots(1,3) 
#
x2.show(ax=ax1)  #MODE2
x1.squeeze().show(ax=ax2)  #MODE1
dls.train.decode((x,))[0].squeeze().show(ax=ax3)
fig.set_figwidth(12)            
fig.set_figheight(12)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

x2=x2.reshape(1,3,512,512)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)
camimg2 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x2).squeeze())

  • CAM

    • mode2에 CAM 결과 올리기
fig, (ax1, ax2) = plt.subplots(1,2) 
x2.squeeze().show(ax=ax1)
ax1.imshow(camimg2[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
x2.squeeze().show(ax=ax2)
ax2.imshow(camimg2[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

- 첫번째, 두번째 CAM결과와 비교
fig, (ax1,ax2,ax3) = plt.subplots(1,3) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax3)
ax3.imshow(camimg2[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

a2=net(x2).tolist()[0][0]
b2=net(x2).tolist()[0][1]
np.exp(a2)/(np.exp(a2)+np.exp(b2)), np.exp(b2)/(np.exp(a2)+np.exp(b2))

(0.026369678868924343, 0.9736303211310757)

4th CNN

  • MODE 3 만들기
test2=camimg2[1]-torch.min(camimg2[1])

A5=torch.exp(-0.06*test2)  

A6=1-A5

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A5.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A6.data.to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

A5.data.to('cpu').shape

torch.Size([16, 16])
X3=np.array(A5.to("cpu").detach(),dtype=np.float32)

Y3=torch.Tensor(cv2.resize(X3,(512,512),interpolation=cv2.INTER_LINEAR))

x3=x2.squeeze().to('cpu')*Y3   #MODE3을 x3로 저장

fig, (ax1,ax2,ax3,ax4) = plt.subplots(1,4) 
#
x3.show(ax=ax1)  #MODE3
x2.squeeze().show(ax=ax2)  #MODE2
x1.squeeze().show(ax=ax3)  #MODE1
dls.train.decode((x,))[0].squeeze().show(ax=ax4)
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

x3=x3.reshape(1,3,512,512)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)
camimg3 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x3).squeeze())

  • CAM

    • mode3에 CAM 결과 올리기
fig, (ax1, ax2) = plt.subplots(1,2) 
x3.squeeze().show(ax=ax1)
ax1.imshow(camimg3[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
x3.squeeze().show(ax=ax2)
ax2.imshow(camimg3[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

- 첫번째, 두번째, 세번째 CAM결과와 비교
fig, (ax1,ax2,ax3,ax4) = plt.subplots(1,4) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax3)
ax3.imshow(camimg2[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
#
dls.train.decode((x,))[0].squeeze().show(ax=ax4)
ax4.imshow(camimg3[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

a3=net(x3).tolist()[0][0]
b3=net(x3).tolist()[0][1]
np.exp(a3)/(np.exp(a3)+np.exp(b3)), np.exp(b3)/(np.exp(a3)+np.exp(b3))

(0.052085219034464114, 0.9479147809655359)
  • CNN, CAM 횟수가 증가함에 따라 exp(-k*test)의 k를 2배씩 해주었음.$\to$ 2배로 증가시키니 k값을 유지했을 때보다 강아지라고 인식한 곳이 잘 이동함.
  • lrnr2 epoch수를 늘려 과적합시킬수록 강아지라고 인식한 곳의 이동이 확연히 드러남.

이미지 수를 늘려보자

1st CNN and CAM 

fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("cat(%s)" % catprob.round(5))
        else: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("dog(%s)" % dogprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()
  • mode1
fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            test=camimg[0]-torch.min(camimg[0])
            A1=torch.exp(-0.015*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1.squeeze().show(ax=ax[i][j])
            ax[i][j].set_title("cat(%s)" % catprob.round(5))
        else: 
            test=camimg[1]-torch.min(camimg[1])
            A1=torch.exp(-0.015*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1.squeeze().show(ax=ax[i][j])
            ax[i][j].set_title("dog(%s)" % dogprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
  • mode1의 residual
fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            test=camimg[0]-torch.min(camimg[0])
            A1=torch.exp(-0.015*test)
            A2=1-A1
            X12=np.array(A2.to("cpu").detach(),dtype=np.float32)
            Y12=torch.Tensor(cv2.resize(X12,(512,512),interpolation=cv2.INTER_LINEAR))
            x12=x.squeeze().to('cpu')*Y12
            x12.squeeze().show(ax=ax[i][j])
            ax[i][j].set_title("cat(%s)" % catprob.round(5))
        else: 
            test=camimg[1]-torch.min(camimg[1])
            A1=torch.exp(-0.015*test)
            A2=1-A1
            X12=np.array(A2.to("cpu").detach(),dtype=np.float32)
            Y12=torch.Tensor(cv2.resize(X12,(512,512),interpolation=cv2.INTER_LINEAR))
            x12=x.squeeze().to('cpu')*Y12
            x12.squeeze().show(ax=ax[i][j])
            ax[i][j].set_title("dog(%s)" % dogprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

2nd CNN and CAM 

fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            test=camimg[0]-torch.min(camimg[0])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            x1.squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg1[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
        else: 
            test=camimg[1]-torch.min(camimg[1])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            x1.squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
  • mode2
fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            test=camimg[0]-torch.min(camimg[0])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            test1=camimg1[1]-torch.min(camimg1[1])
            A3=torch.exp(-0.03*test1) 
            X2=np.array(A3.to("cpu").detach(),dtype=np.float32)
            Y2=torch.Tensor(cv2.resize(X2,(512,512),interpolation=cv2.INTER_LINEAR))
            x2=x1.squeeze().to('cpu')*Y2 
            x2.squeeze().show(ax=ax[i][j])
        else: 
            test=camimg[1]-torch.min(camimg[1])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            test1=camimg1[1]-torch.min(camimg1[1])
            A3=torch.exp(-0.03*test1) 
            X2=np.array(A3.to("cpu").detach(),dtype=np.float32)
            Y2=torch.Tensor(cv2.resize(X2,(512,512),interpolation=cv2.INTER_LINEAR))
            x2=x1.squeeze().to('cpu')*Y2 
            x2.squeeze().show(ax=ax[i][j])
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
  • mode2의 residual
fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x.to('cpu')).squeeze())
        a,b = net(x.to('cpu')).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            test=camimg[0]-torch.min(camimg[0])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            test1=camimg1[1]-torch.min(camimg1[1])
            A3=torch.exp(-0.03*test1)
            A4=1-A3
            X22=np.array(A4.to("cpu").detach(),dtype=np.float32)
            Y22=torch.Tensor(cv2.resize(X22,(512,512),interpolation=cv2.INTER_LINEAR))
            x22=x1.squeeze().to('cpu')*Y22 
            x22.squeeze().show(ax=ax[i][j])
        else: 
            test=camimg[1]-torch.min(camimg[1])
            A1=torch.exp(-0.03*test)
            X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
            Y1=torch.Tensor(cv2.resize(X1,(512,512),interpolation=cv2.INTER_LINEAR))
            x1=x.squeeze().to('cpu')*Y1
            x1=x1.reshape(1,3,512,512)
            camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())
            test1=camimg1[1]-torch.min(camimg1[1])
            A3=torch.exp(-0.03*test1) 
            A4=1-A3
            X22=np.array(A4.to("cpu").detach(),dtype=np.float32)
            Y22=torch.Tensor(cv2.resize(X22,(512,512),interpolation=cv2.INTER_LINEAR))
            x22=x1.squeeze().to('cpu')*Y22 
            x22.squeeze().show(ax=ax[i][j])
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())
        a,b = net(x).tolist()[0]
        catprob, dogprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if catprob>dogprob: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("cat(%s)" % catprob.round(5))
        else: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,511,511,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("dog(%s)" % dogprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_1490982/3407106732.py in <module>
----> 1 fig, ax = plt.subplots(5,5)
      2 k=0
      3 for i in range(5):
      4     for j in range(5):
      5         x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))

NameError: name 'plt' is not defined